Overview

Dataset statistics

Number of variables25
Number of observations17000
Missing cells856
Missing cells (%)0.2%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory3.2 MiB
Average record size in memory200.0 B

Variable types

NUM13
CAT11
BOOL1

Warnings

Dataset has 3 (< 0.1%) duplicate rows Duplicates
Hotel_Address has a high cardinality: 1365 distinct values High cardinality
Review_Date has a high cardinality: 731 distinct values High cardinality
Hotel_Name has a high cardinality: 1364 distinct values High cardinality
Negative_Review has a high cardinality: 11586 distinct values High cardinality
Positive_Review has a high cardinality: 14193 distinct values High cardinality
Tags has a high cardinality: 7413 distinct values High cardinality
days_since_review has a high cardinality: 731 distinct values High cardinality
Review has a high cardinality: 16727 distinct values High cardinality
Travel_type has 556 (3.3%) missing values Missing
Reviewer_Nationality is uniformly distributed Uniform
Review is uniformly distributed Uniform
Review_Total_Negative_Word_Counts has 4270 (25.1%) zeros Zeros
Review_Total_Positive_Word_Counts has 1350 (7.9%) zeros Zeros
Review_sent has 3948 (23.2%) zeros Zeros
Positive_Review_sent has 456 (2.7%) zeros Zeros
Negative_Review_sent has 705 (4.1%) zeros Zeros

Reproduction

Analysis started2020-12-09 14:56:02.817926
Analysis finished2020-12-09 14:56:34.042330
Duration31.22 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Hotel_Address
Categorical

HIGH CARDINALITY

Distinct1365
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
27 Devonshire Terrace Westminster Borough London W2 3DP United Kingdom
 
88
Scarsdale Place Kensington Kensington and Chelsea London W8 5SY United Kingdom
 
85
225 Edgware Road Westminster Borough London W2 1JU United Kingdom
 
83
372 Strand Westminster Borough London WC2R 0JJ United Kingdom
 
76
Great Cumberland Place Westminster Borough London W1H 7DL United Kingdom
 
75
Other values (1360)
16593 
ValueCountFrequency (%) 
27 Devonshire Terrace Westminster Borough London W2 3DP United Kingdom880.5%
 
Scarsdale Place Kensington Kensington and Chelsea London W8 5SY United Kingdom850.5%
 
225 Edgware Road Westminster Borough London W2 1JU United Kingdom830.5%
 
372 Strand Westminster Borough London WC2R 0JJ United Kingdom760.4%
 
Great Cumberland Place Westminster Borough London W1H 7DL United Kingdom750.4%
 
30 Leinster Gardens Bayswater Westminster Borough London W2 3AN United Kingdom730.4%
 
163 Marsh Wall Docklands Tower Hamlets London E14 9SJ United Kingdom670.4%
 
Piazza Duca D Aosta 4 6 Central Station 20124 Milan Italy660.4%
 
Delflandlaan 15 Slotervaart 1062 EA Amsterdam Netherlands660.4%
 
Bryanston Street Marble Arch Westminster Borough London W1H 7EH United Kingdom650.4%
 
Other values (1355)1625695.6%
 
2020-12-09T14:56:34.164607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique85 ?
Unique (%)0.5%
2020-12-09T14:56:34.380306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length96
Median length57
Mean length58.11417647
Min length34

Additional_Number_of_Scoring
Real number (ℝ≥0)

Distinct469
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.1751176
Minimum5
Maximum2682
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:34.605980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile49
Q1129
median256
Q3482
95-th percentile1190
Maximum2682
Range2677
Interquartile range (IQR)353

Descriptive statistics

Standard deviation383.7724891
Coefficient of variation (CV)1.006814116
Kurtosis8.43356199
Mean381.1751176
Median Absolute Deviation (MAD)151
Skewness2.472178131
Sum6479977
Variance147281.3234
MonotocityNot monotonic
2020-12-09T14:56:34.817794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2111120.7%
 
2281080.6%
 
2561050.6%
 
391970.6%
 
74950.6%
 
387940.6%
 
325940.6%
 
493910.5%
 
101900.5%
 
207900.5%
 
Other values (459)1602494.3%
 
ValueCountFrequency (%) 
55< 0.1%
 
65< 0.1%
 
74< 0.1%
 
84< 0.1%
 
94< 0.1%
 
ValueCountFrequency (%) 
2682670.4%
 
2288760.4%
 
1936520.3%
 
1831850.5%
 
1485830.5%
 

Review_Date
Categorical

HIGH CARDINALITY

Distinct731
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
8/2/2017
 
80
9/15/2016
 
76
4/5/2017
 
66
10/27/2015
 
56
8/14/2016
 
55
Other values (726)
16667 
ValueCountFrequency (%) 
8/2/2017800.5%
 
9/15/2016760.4%
 
4/5/2017660.4%
 
10/27/2015560.3%
 
8/14/2016550.3%
 
3/29/2016550.3%
 
9/29/2015550.3%
 
11/3/2015540.3%
 
8/18/2015530.3%
 
8/30/2016510.3%
 
Other values (721)1639996.5%
 
2020-12-09T14:56:35.050591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:56:35.259463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length9
Mean length8.936764706
Min length8

Average_Score
Real number (ℝ≥0)

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.403347059
Minimum6.4
Maximum9.8
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:35.445683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile7.5
Q18.1
median8.4
Q38.8
95-th percentile9.2
Maximum9.8
Range3.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5322064392
Coefficient of variation (CV)0.06333267393
Kurtosis0.2225766328
Mean8.403347059
Median Absolute Deviation (MAD)0.3
Skewness-0.467020344
Sum142856.9
Variance0.2832436939
MonotocityNot monotonic
2020-12-09T14:56:35.650027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
8.514848.7%
 
8.413588.0%
 
8.613187.8%
 
8.112207.2%
 
8.212197.2%
 
8.810366.1%
 
8.710186.0%
 
8.39935.8%
 
8.99325.5%
 
97514.4%
 
Other values (23)567133.4%
 
ValueCountFrequency (%) 
6.4270.2%
 
6.6120.1%
 
6.7280.2%
 
6.8380.2%
 
6.9560.3%
 
ValueCountFrequency (%) 
9.83< 0.1%
 
9.6300.2%
 
9.5500.3%
 
9.42311.4%
 
9.34452.6%
 

Hotel_Name
Categorical

HIGH CARDINALITY

Distinct1364
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
Park Grand Paddington Court
 
88
Copthorne Tara Hotel London Kensington
 
85
Hilton London Metropole
 
83
Strand Palace Hotel
 
76
The Cumberland A Guoman Hotel
 
75
Other values (1359)
16593 
ValueCountFrequency (%) 
Park Grand Paddington Court880.5%
 
Copthorne Tara Hotel London Kensington850.5%
 
Hilton London Metropole830.5%
 
Strand Palace Hotel760.4%
 
The Cumberland A Guoman Hotel750.4%
 
Blakemore Hyde Park730.4%
 
Britannia International Hotel Canary Wharf670.4%
 
Glam Milano660.4%
 
Best Western Premier Hotel Couture660.4%
 
Amba Hotel Marble Arch650.4%
 
Other values (1354)1625695.6%
 
2020-12-09T14:56:36.409047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique85 ?
Unique (%)0.5%
2020-12-09T14:56:36.662137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length60
Median length23
Mean length24.61535294
Min length2

Reviewer_Nationality
Categorical

UNIFORM

Distinct34
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
Sweden
 
500
Kuwait
 
500
South Africa
 
500
Poland
 
500
Singapore
 
500
Other values (29)
14500 
ValueCountFrequency (%) 
Sweden 5002.9%
 
Kuwait 5002.9%
 
South Africa 5002.9%
 
Poland 5002.9%
 
Singapore 5002.9%
 
Belgium 5002.9%
 
Switzerland 5002.9%
 
United States of America 5002.9%
 
Israel 5002.9%
 
Norway 5002.9%
 
Other values (24)1200070.6%
 
2020-12-09T14:56:36.879423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:56:37.088749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length9
Mean length10.47058824
Min length7

Negative_Review
Categorical

HIGH CARDINALITY

Distinct11586
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
No Negative
4270 
Nothing
 
422
Nothing
 
122
nothing
 
77
 
43
Other values (11581)
12066 
ValueCountFrequency (%) 
No Negative427025.1%
 
Nothing4222.5%
 
Nothing 1220.7%
 
nothing770.5%
 
430.3%
 
None230.1%
 
Small room230.1%
 
Breakfast210.1%
 
Location190.1%
 
none160.1%
 
Other values (11576)1196470.4%
 
2020-12-09T14:56:37.347641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11398 ?
Unique (%)67.0%
2020-12-09T14:56:37.600818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1951
Median length40
Mean length92.18141176
Min length1

Review_Total_Negative_Word_Counts
Real number (ℝ≥0)

ZEROS

Distinct240
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.16629412
Minimum0
Maximum380
Zeros4270
Zeros (%)25.1%
Memory size132.8 KiB
2020-12-09T14:56:37.835559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q323
95-th percentile68
Maximum380
Range380
Interquartile range (IQR)23

Descriptive statistics

Standard deviation29.14347291
Coefficient of variation (CV)1.604260766
Kurtosis29.68266722
Mean18.16629412
Median Absolute Deviation (MAD)9
Skewness4.304346386
Sum308827
Variance849.3420134
MonotocityNot monotonic
2020-12-09T14:56:38.065753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0427025.1%
 
28194.8%
 
66013.5%
 
75653.3%
 
35473.2%
 
55393.2%
 
45093.0%
 
95063.0%
 
84902.9%
 
104132.4%
 
Other values (230)774145.5%
 
ValueCountFrequency (%) 
0427025.1%
 
28194.8%
 
35473.2%
 
45093.0%
 
55393.2%
 
ValueCountFrequency (%) 
3801< 0.1%
 
3761< 0.1%
 
3752< 0.1%
 
3651< 0.1%
 
3611< 0.1%
 

Total_Number_of_Reviews
Real number (ℝ≥0)

Distinct1061
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2418.115235
Minimum59
Maximum9568
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:38.272899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile392
Q11057
median1901
Q33342
95-th percentile6512.4
Maximum9568
Range9509
Interquartile range (IQR)2285

Descriptive statistics

Standard deviation1814.168768
Coefficient of variation (CV)0.7502408247
Kurtosis1.656460075
Mean2418.115235
Median Absolute Deviation (MAD)1006
Skewness1.298354301
Sum41107959
Variance3291208.32
MonotocityNot monotonic
2020-12-09T14:56:38.482836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6608880.5%
 
7105850.5%
 
6977830.5%
 
9568760.4%
 
5180750.4%
 
6596730.4%
 
9086670.4%
 
7371660.4%
 
8177660.4%
 
5770650.4%
 
Other values (1051)1625695.6%
 
ValueCountFrequency (%) 
594< 0.1%
 
676< 0.1%
 
691< 0.1%
 
703< 0.1%
 
761< 0.1%
 
ValueCountFrequency (%) 
9568760.4%
 
9086670.4%
 
8177660.4%
 
7586570.3%
 
7491520.3%
 

Positive_Review
Categorical

HIGH CARDINALITY

Distinct14193
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
No Positive
 
1350
Location
 
378
location
 
77
Everything
 
73
The location
 
56
Other values (14188)
15066 
ValueCountFrequency (%) 
No Positive13507.9%
 
Location3782.2%
 
location770.5%
 
Everything730.4%
 
The location560.3%
 
Nothing460.3%
 
Good location390.2%
 
Great location350.2%
 
Location 300.2%
 
Breakfast240.1%
 
Other values (14183)1489287.6%
 
2020-12-09T14:56:38.732989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13891 ?
Unique (%)81.7%
2020-12-09T14:56:39.003800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1955
Median length54
Mean length90.05617647
Min length1

Review_Total_Positive_Word_Counts
Real number (ℝ≥0)

ZEROS

Distinct186
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.88029412
Minimum0
Maximum373
Zeros1350
Zeros (%)7.9%
Memory size132.8 KiB
2020-12-09T14:56:39.267588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q321
95-th percentile54
Maximum373
Range373
Interquartile range (IQR)16

Descriptive statistics

Standard deviation21.23786636
Coefficient of variation (CV)1.258145516
Kurtosis30.02590695
Mean16.88029412
Median Absolute Deviation (MAD)7
Skewness4.072515925
Sum286965
Variance451.0469674
MonotocityNot monotonic
2020-12-09T14:56:39.485734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
013507.9%
 
59825.8%
 
69295.5%
 
38655.1%
 
48384.9%
 
28344.9%
 
78054.7%
 
96834.0%
 
86633.9%
 
106193.6%
 
Other values (176)843249.6%
 
ValueCountFrequency (%) 
013507.9%
 
28344.9%
 
38655.1%
 
48384.9%
 
59825.8%
 
ValueCountFrequency (%) 
3731< 0.1%
 
3081< 0.1%
 
2911< 0.1%
 
2891< 0.1%
 
2811< 0.1%
 
Distinct126
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.639470588
Minimum1
Maximum315
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:39.744560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile33
Maximum315
Range314
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.76445716
Coefficient of variation (CV)1.427926672
Kurtosis39.78164827
Mean9.639470588
Median Absolute Deviation (MAD)4
Skewness4.538576177
Sum163871
Variance189.4602809
MonotocityNot monotonic
2020-12-09T14:56:39.966916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1367421.6%
 
2176810.4%
 
314248.4%
 
411476.7%
 
59315.5%
 
68254.9%
 
77144.2%
 
86383.8%
 
95433.2%
 
104982.9%
 
Other values (116)483828.5%
 
ValueCountFrequency (%) 
1367421.6%
 
2176810.4%
 
314248.4%
 
411476.7%
 
59315.5%
 
ValueCountFrequency (%) 
3151< 0.1%
 
1991< 0.1%
 
1911< 0.1%
 
1731< 0.1%
 
1711< 0.1%
 

Reviewer_Score
Real number (ℝ≥0)

Distinct35
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.234694118
Minimum2.5
Maximum10
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:40.178924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4.6
Q17.5
median8.8
Q39.6
95-th percentile10
Maximum10
Range7.5
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.677791833
Coefficient of variation (CV)0.2037467098
Kurtosis0.6766519468
Mean8.234694118
Median Absolute Deviation (MAD)0.9
Skewness-1.088897042
Sum139989.8
Variance2.814985435
MonotocityNot monotonic
2020-12-09T14:56:40.403347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
10313518.4%
 
9.6214612.6%
 
9.2193011.4%
 
8.816579.7%
 
8.315048.8%
 
7.511957.0%
 
7.911857.0%
 
7.18655.1%
 
6.76814.0%
 
6.35773.4%
 
Other values (25)212512.5%
 
ValueCountFrequency (%) 
2.5770.5%
 
2.9680.4%
 
32< 0.1%
 
3.31120.7%
 
3.56< 0.1%
 
ValueCountFrequency (%) 
10313518.4%
 
9.6214612.6%
 
9.5320.2%
 
9.2193011.4%
 
9260.2%
 

Tags
Categorical

HIGH CARDINALITY

Distinct7413
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']
 
68
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']
 
66
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 2 nights ']
 
64
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ']
 
63
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']
 
59
Other values (7408)
16680 
ValueCountFrequency (%) 
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']680.4%
 
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']660.4%
 
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 2 nights ']640.4%
 
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ']630.4%
 
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']590.3%
 
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']570.3%
 
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']560.3%
 
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']550.3%
 
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ']550.3%
 
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 1 night ']550.3%
 
Other values (7403)1640296.5%
 
2020-12-09T14:56:40.688328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5253 ?
Unique (%)30.9%
2020-12-09T14:56:40.983916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length213
Median length105
Mean length101.7076471
Min length30

days_since_review
Categorical

HIGH CARDINALITY

Distinct731
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
1 days
 
80
322 day
 
76
120 day
 
66
646 day
 
56
354 day
 
55
Other values (726)
16667 
ValueCountFrequency (%) 
1 days800.5%
 
322 day760.4%
 
120 day660.4%
 
646 day560.3%
 
354 day550.3%
 
674 day550.3%
 
492 day550.3%
 
639 day540.3%
 
716 day530.3%
 
338 day510.3%
 
Other values (721)1639996.5%
 
2020-12-09T14:56:41.265231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:56:41.485756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.983823529
Min length6

lat
Real number (ℝ≥0)

Distinct1349
Distinct (%)8.0%
Missing147
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean48.70350193
Minimum41.3283758
Maximum52.4001813
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:41.731218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum41.3283758
5-th percentile41.384829
Q148.1756283
median48.8793427
Q351.5144004
95-th percentile52.3702469
Maximum52.4001813
Range11.0718055
Interquartile range (IQR)3.3387721

Descriptive statistics

Standard deviation3.688259769
Coefficient of variation (CV)0.07572884131
Kurtosis-0.3447617847
Mean48.70350193
Median Absolute Deviation (MAD)2.6346902
Skewness-0.9779669749
Sum820800.118
Variance13.60326012
MonotocityNot monotonic
2020-12-09T14:56:41.969627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
51.5135555880.5%
 
51.499046850.5%
 
51.5195688830.5%
 
51.5110993760.4%
 
51.514879750.4%
 
51.5129736730.4%
 
51.5019097670.4%
 
52.3511137660.4%
 
45.4838504660.4%
 
52.3681299650.4%
 
Other values (1339)1610994.8%
 
(Missing)1470.9%
 
ValueCountFrequency (%) 
41.3283758250.1%
 
41.368437260.2%
 
41.37030418< 0.1%
 
41.371308530.3%
 
41.37252466< 0.1%
 
ValueCountFrequency (%) 
52.4001813110.1%
 
52.3924898100.1%
 
52.39236843< 0.1%
 
52.3872884250.1%
 
52.3856494540.3%
 

lng
Real number (ℝ)

Distinct1349
Distinct (%)8.0%
Missing147
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean3.965988724
Minimum-0.3192925
Maximum16.4217627
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:42.201330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.3192925
5-th percentile-0.1882164
Q1-0.1283489
median2.211756
Q34.8943658
95-th percentile16.3710205
Maximum16.4217627
Range16.7410552
Interquartile range (IQR)5.0227147

Descriptive statistics

Standard deviation5.207436922
Coefficient of variation (CV)1.313023633
Kurtosis0.9047414419
Mean3.965988724
Median Absolute Deviation (MAD)2.3683066
Skewness1.44714942
Sum66838.80797
Variance27.1173993
MonotocityNot monotonic
2020-12-09T14:56:42.453421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.180002880.5%
 
-0.1917073850.5%
 
-0.170521830.5%
 
-0.1208673760.4%
 
-0.1606501750.4%
 
-0.1837431730.4%
 
-0.0232208670.4%
 
4.8411629660.4%
 
9.2034067660.4%
 
-0.1565506650.4%
 
Other values (1339)1610994.8%
 
(Missing)1470.9%
 
ValueCountFrequency (%) 
-0.31929256< 0.1%
 
-0.3060711< 0.1%
 
-0.29150525< 0.1%
 
-0.290706110.1%
 
-0.2864945210.1%
 
ValueCountFrequency (%) 
16.4217627270.2%
 
16.4200957310.2%
 
16.4133973110.1%
 
16.4129493320.2%
 
16.411699790.1%
 

Review
Categorical

HIGH CARDINALITY
UNIFORM

Distinct16727
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
Location. No Negative
 
41
Everything. Nothing
 
35
Everything. No Negative
 
16
location. No Negative
 
11
Everything . No Negative
 
11
Other values (16722)
16886 
ValueCountFrequency (%) 
Location. No Negative410.2%
 
Everything. Nothing350.2%
 
Everything. No Negative160.1%
 
location. No Negative110.1%
 
Everything . No Negative110.1%
 
Location. Nothing100.1%
 
Great location. No Negative8< 0.1%
 
No Positive. No Negative7< 0.1%
 
Great location . No Negative6< 0.1%
 
Everything was perfect . No Negative6< 0.1%
 
Other values (16717)1684999.1%
 
2020-12-09T14:56:42.757077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique16639 ?
Unique (%)97.9%
2020-12-09T14:56:43.014588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3231
Median length123
Mean length184.2375882
Min length4

Review_sent
Real number (ℝ)

ZEROS

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1335000052
Minimum-0.8000000119
Maximum0.8999999762
Zeros3948
Zeros (%)23.2%
Memory size132.8 KiB
2020-12-09T14:56:43.233020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8000000119
5-th percentile-0.6999999881
Q10
median0.1000000015
Q30.5
95-th percentile0.6000000238
Maximum0.8999999762
Range1.699999988
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3807389717
Coefficient of variation (CV)2.851977206
Kurtosis-0.1105772972
Mean0.1335000052
Median Absolute Deviation (MAD)0.2000000104
Skewness-0.4567576258
Sum2269.500088
Variance0.1449621646
MonotocityNot monotonic
2020-12-09T14:56:43.421752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
0394823.2%
 
0.6000000238341120.1%
 
0.1000000015206512.1%
 
0.20000000314058.3%
 
0.30000001198695.1%
 
-0.69999998817334.3%
 
-0.10000000157264.3%
 
0.56864.0%
 
0.4000000066353.7%
 
-0.2000000036043.6%
 
Other values (8)191811.3%
 
ValueCountFrequency (%) 
-0.80000001193141.8%
 
-0.69999998817334.3%
 
-0.60000002382821.7%
 
-0.52231.3%
 
-0.4000000062721.6%
 
ValueCountFrequency (%) 
0.89999997621701.0%
 
0.80000001191300.8%
 
0.69999998811260.7%
 
0.6000000238341120.1%
 
0.56864.0%
 

Positive_Review_sent
Real number (ℝ)

ZEROS

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5839235197
Minimum-0.8999999762
Maximum0.8999999762
Zeros456
Zeros (%)2.7%
Memory size132.8 KiB
2020-12-09T14:56:43.633499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8999999762
5-th percentile-0.6999999881
Q10.400000006
median0.8999999762
Q30.8999999762
95-th percentile0.8999999762
Maximum0.8999999762
Range1.799999952
Interquartile range (IQR)0.4999999702

Descriptive statistics

Standard deviation0.5021722407
Coefficient of variation (CV)0.8599965986
Kurtosis1.357222316
Mean0.5839235197
Median Absolute Deviation (MAD)0
Skewness-1.618109622
Sum9926.699834
Variance0.2521769594
MonotocityNot monotonic
2020-12-09T14:56:43.815070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
0.8999999762897352.8%
 
0.8000000119185810.9%
 
-0.699999988114178.3%
 
0.4000000068505.0%
 
0.69999998817554.4%
 
0.2000000035163.0%
 
04562.7%
 
0.60000002384392.6%
 
0.30000001194112.4%
 
0.53802.2%
 
Other values (9)9455.6%
 
ValueCountFrequency (%) 
-0.89999997624< 0.1%
 
-0.8000000119640.4%
 
-0.699999988114178.3%
 
-0.6000000238600.4%
 
-0.5600.4%
 
ValueCountFrequency (%) 
0.8999999762897352.8%
 
0.8000000119185810.9%
 
0.69999998817554.4%
 
0.60000002384392.6%
 
0.53802.2%
 

Negative_Review_sent
Real number (ℝ)

ZEROS

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2768941173
Minimum-0.8999999762
Maximum0.8999999762
Zeros705
Zeros (%)4.1%
Memory size132.8 KiB
2020-12-09T14:56:44.019770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8999999762
5-th percentile-0.8000000119
Q1-0.6999999881
median-0.400000006
Q30.3000000119
95-th percentile0.3000000119
Maximum0.8999999762
Range1.799999952
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4705307075
Coefficient of variation (CV)-1.699316374
Kurtosis-1.245651457
Mean-0.2768941173
Median Absolute Deviation (MAD)0.400000006
Skewness0.4383320891
Sum-4707.199995
Variance0.2213991467
MonotocityNot monotonic
2020-12-09T14:56:44.212450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
0.3000000119446726.3%
 
-0.6999999881301517.7%
 
-0.8000000119281816.6%
 
-0.600000023815439.1%
 
-0.40000000611756.9%
 
-0.58384.9%
 
07054.1%
 
-0.30000001194872.9%
 
-0.2000000034462.6%
 
-0.10000000153722.2%
 
Other values (9)11346.7%
 
ValueCountFrequency (%) 
-0.8999999762740.4%
 
-0.8000000119281816.6%
 
-0.6999999881301517.7%
 
-0.600000023815439.1%
 
-0.58384.9%
 
ValueCountFrequency (%) 
0.89999997621681.0%
 
0.8000000119950.6%
 
0.6999999881740.4%
 
0.6000000238620.4%
 
0.5750.4%
 

Travel_type
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing556
Missing (%)3.3%
Memory size132.8 KiB
leisure
12842 
business
3602 
ValueCountFrequency (%) 
leisure1284275.5%
 
business360221.2%
 
(Missing)5563.3%
 
2020-12-09T14:56:44.429717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:56:44.584167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:44.685652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length7.081058824
Min length3
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
1
9146 
0
7854 
ValueCountFrequency (%) 
1914653.8%
 
0785446.2%
 
2020-12-09T14:56:44.872570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

N_Persons
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
couple
6841 
solo
4549 
family
3485 
group
2125 
ValueCountFrequency (%) 
couple684140.2%
 
solo454926.8%
 
family348520.5%
 
group212512.5%
 
2020-12-09T14:56:44.964153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:56:45.114857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:45.231430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.339823529
Min length4

Stayed_Nights
Real number (ℝ≥0)

Distinct22
Distinct (%)0.1%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.823761328
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Memory size132.8 KiB
2020-12-09T14:56:45.435328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.884769165
Coefficient of variation (CV)0.6674675888
Kurtosis11.65129454
Mean2.823761328
Median Absolute Deviation (MAD)1
Skewness2.247220293
Sum47987
Variance3.552354804
MonotocityNot monotonic
2020-12-09T14:56:45.660434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
1439825.9%
 
2433625.5%
 
3372421.9%
 
4216512.7%
 
510456.1%
 
65433.2%
 
73942.3%
 
81560.9%
 
9860.5%
 
10510.3%
 
Other values (12)960.6%
 
ValueCountFrequency (%) 
1439825.9%
 
2433625.5%
 
3372421.9%
 
4216512.7%
 
510456.1%
 
ValueCountFrequency (%) 
281< 0.1%
 
272< 0.1%
 
221< 0.1%
 
192< 0.1%
 
181< 0.1%
 

Interactions

2020-12-09T14:56:06.281071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:06.415803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:06.553400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:06.698472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:06.844369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:06.997238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:07.136672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:07.277543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:07.409759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:07.545888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:07.695328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:07.851387image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:08.022904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:08.160900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:08.306592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:08.448764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:08.604088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:08.742742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:08.934977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:09.083106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:09.227046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:09.366406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:09.506833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:09.657159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:09.802628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:09.961359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:10.100536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:10.581929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:10.730612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:10.894857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:11.043834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:11.196174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:11.347970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:11.497105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:11.643857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:11.789189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:11.953837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:12.103899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:12.255298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:12.397932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:12.533349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:12.677140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:12.819367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:12.964389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:13.108716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:13.250198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:13.390394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:13.520941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:13.664748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:13.802722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:13.957857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:14.100544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:14.233164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:14.378739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:14.525179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:14.677334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:14.822352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:14.986835image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:15.141415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:15.290637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:15.436966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:15.597499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:15.766891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:15.922814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:16.087630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:16.233200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:16.374008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:16.517187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:16.665745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:16.807170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:16.986189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:17.135803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:17.282841image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:17.421838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:17.565254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:17.726729image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:17.881359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:18.075590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:18.218712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:18.361911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:18.511878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:18.663408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:18.817739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-09T14:56:19.138737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:19.288016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:19.428957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:19.576303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-09T14:56:20.027873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:20.182327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:20.315076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:20.450432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:20.590520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:20.722394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:20.863312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:21.019008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:21.168588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:21.308376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:21.444171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.002764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.152228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.291422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.421740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.560679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.703259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.847845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:22.992342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:23.149071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:23.300785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:23.442000image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:23.576929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:23.717107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:23.858326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:24.011593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:24.159675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:24.294612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:24.433544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:24.574045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:24.719919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:24.858119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:25.009988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:25.156461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:25.312684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:25.450286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:25.607894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:25.763606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:25.910458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:26.054818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:26.203054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:26.345435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:26.489407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:26.639070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:26.779985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:26.933576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:27.079635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:27.234693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:27.371655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:27.512431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:27.665579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:27.814469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:27.990593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:28.150620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-09T14:56:28.755432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:28.931698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:29.077694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:29.225277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:29.373065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:29.515272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:29.664182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:29.809398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:29.958956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:30.101267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:30.234622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:30.381127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:30.523159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:30.656385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:30.796996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:30.940279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:31.079468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:31.210321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:31.356727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:31.493205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:31.645030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:31.782326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-09T14:56:45.862323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-09T14:56:46.145134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-09T14:56:46.420005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-09T14:56:46.707694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-09T14:56:47.001581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-09T14:56:32.143260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:33.064987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:33.483538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:56:33.700293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Hotel_AddressAdditional_Number_of_ScoringReview_DateAverage_ScoreHotel_NameReviewer_NationalityNegative_ReviewReview_Total_Negative_Word_CountsTotal_Number_of_ReviewsPositive_ReviewReview_Total_Positive_Word_CountsTotal_Number_of_Reviews_Reviewer_Has_GivenReviewer_ScoreTagsdays_since_reviewlatlngReviewReview_sentPositive_Review_sentNegative_Review_sentTravel_typeFrom_MobileN_PersonsStayed_Nights
08 Avenue Rapp 7th arr 75007 Paris France747/5/20168.9Derby AlmaUnited KingdomParking But it s all the same in any city I suppose expensive and rare to find a space Lol21509Cracking view of the tower7.03.08.3[' Leisure trip ', ' Family with young children ', ' Family Suite ', ' Stayed 1 night ', ' Submitted from a mobile device ']394 day48.8605032.300661Cracking view of the tower . Parking But it s all the same in any city I suppose expensive and rare to find a space Lol0.20.8-0.2leisure1.0family1.0
1Lakeside Way Brent London HA9 0BU United Kingdom142712/14/20168.8Hilton London WembleyUnited KingdomThe second night with X factor personnel staying at the hotel I was denied entry to the hotel as I was wearing jeans and a t shirt and a Santa hat as I had been a guest at the royal Albert hall On my return to the hotel the entrance was cordoned off due to somebody called Sharon Osborn being in the hotel lobby I had to wait over 20 minutes for security to eventually say if you are actually a guest you should have a key card I showed him my key card and eventually he let me in Then once entering the hotel was stopped again by X factor staff wanting to know why I was coming into the hotel I had to show my key card again to prove I was a guest My second evening was ruined by staff at the Hilton Wembley and security staff1524305First night was fine second night was a nightmare as some staff and judges from the X factor were staying in the hotel25.01.07.9[' Leisure trip ', ' Group ', ' Deluxe King Room with Stadium View ', ' Stayed 2 nights ', ' Submitted from a mobile device ']232 day51.557696-0.283526First night was fine second night was a nightmare as some staff and judges from the X factor were staying in the hotel . The second night with X factor personnel staying at the hotel I was denied entry to the hotel as I was wearing jeans and a t shirt and a Santa hat as I had been a guest at the royal Albert hall On my return to the hotel the entrance was cordoned off due to somebody called Sharon Osborn being in the hotel lobby I had to wait over 20 minutes for security to eventually say if you are actually a guest you should have a key card I showed him my key card and eventually he let me in Then once entering the hotel was stopped again by X factor staff wanting to know why I was coming into the hotel I had to show my key card again to prove I was a guest My second evening was ruined by staff at the Hilton Wembley and security staff-0.7-0.7-0.7leisure1.0group2.0
2225 Edgware Road Westminster Borough London W2 1JU United Kingdom14858/30/20167.5Hilton London MetropoleUnited KingdomRude staff room not ready at 3 30 pm Request early check in and late check out not accept216977No Positive0.02.04.6[' Leisure trip ', ' Family with young children ', ' King Hilton Guest Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']338 day51.519569-0.170521No Positive. Rude staff room not ready at 3 30 pm Request early check in and late check out not accept-0.7-0.7-0.7leisure1.0family1.0
31 3 Queens Garden Westminster Borough London W2 3BA United Kingdom10585/28/20177.7The Park Grand London PaddingtonUnited KingdomNo Negative04380The hotel was perfect for a short stay in the heart of London very conveniently located for Paddington station and the Heathrow Express Highly recommended27.02.08.8[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 1 night ']67 days51.514218-0.180903The hotel was perfect for a short stay in the heart of London very conveniently located for Paddington station and the Heathrow Express Highly recommended . No Negative0.60.90.3leisure0.0couple1.0
4Via Mauro Macchi 1 Central Station 20124 Milan Italy6511/2/20158.4Hotel MediolanumUnited KingdomUnfriendly staff3885The furniture3.017.05.8[' Leisure trip ', ' Group ', ' Double or Twin Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']640 day45.4813429.203949The furniture. Unfriendly staff-0.30.1-0.8leisure1.0group3.0
526 28 Trebovir Rd Kensington and Chelsea London SW5 9NJ United Kingdom3289/9/20167.5Mayflower Hotel ApartmentsUnited KingdomThe quilt bedding had two stains on when i went to reception the did give me another cover leaving me to change the bedding The rooms didn t have air conditioning which we knew about when booking The fan left in our room was no use and the room was like a sauna which wouldn t of been so bad if our window wasn t level with the garden seating so everyone could see in meaning we couldn t really leave the curtains and windows open to deal with the heat issue The floor in the room was incredibly creaky so it made it impossible to get up in the night to go to the bathroom without waking your partner up The hallway flooring was also very creaky also so you could hear people walking past our room to get to theirs1442197Location is brilliant near tube station and cheap for london free wifi The place overall presented very clean and smart22.02.06.3[' Leisure trip ', ' Couple ', ' Deluxe Double Room with Four Poster Bed ', ' Stayed 3 nights ']328 day51.491668-0.194747Location is brilliant near tube station and cheap for london free wifi The place overall presented very clean and smart . The quilt bedding had two stains on when i went to reception the did give me another cover leaving me to change the bedding The rooms didn t have air conditioning which we knew about when booking The fan left in our room was no use and the room was like a sauna which wouldn t of been so bad if our window wasn t level with the garden seating so everyone could see in meaning we couldn t really leave the curtains and windows open to deal with the heat issue The floor in the room was incredibly creaky so it made it impossible to get up in the night to go to the bathroom without waking your partner up The hallway flooring was also very creaky also so you could hear people walking past our room to get to theirs0.00.9-0.8leisure0.0couple3.0
652 56 Inverness Terrace Westminster Borough London W2 3LB United Kingdom5457/2/20178.0Shaftesbury Hyde Park InternationalUnited KingdomThe luxury room we stayed in the cost 176 per night without breakfast was no bigger than 7ft square it was absolutely ridiculous We had continuous buzzing noise all night and the towel rail was hanging off It was a total rip off452907Decorating was the only nice part of the room10.02.03.3[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']32 days51.512397-0.186124Decorating was the only nice part of the room. The luxury room we stayed in the cost 176 per night without breakfast was no bigger than 7ft square it was absolutely ridiculous We had continuous buzzing noise all night and the towel rail was hanging off It was a total rip off-0.6-0.4-0.7leisure1.0couple1.0
7John M Keynesplein 2 Slotervaart 1066 EP Amsterdam Netherlands35012/2/20167.9Dutch Design Hotel ArtemisUnited Kingdomshould have been a bit more cleaner82167Nice friendly staff4.03.07.5[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']244 day52.3417894.824952Nice friendly staff. should have been a bit more cleaner0.10.9-0.7leisure1.0couple2.0
815 Beeston Place Westminster Borough London SW1W 0JW United Kingdom677/7/20179.4The GoringUnited KingdomNo seperate shower cubicle5200Conceirge doorman lovely bar5.01.08.8[' Leisure trip ', ' Couple ', ' Delightful Queen Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']27 days51.497562-0.145551Conceirge doorman lovely bar. No seperate shower cubicle0.10.9-0.6leisure1.0couple1.0
9Excel 2 Festoon Way Royal Victoria Dock Newham London E16 1RH United Kingdom8539/15/20168.4DoubleTree By Hilton London ExcelUnited KingdomNo Negative02726Very good and clean5.01.07.9[' Leisure trip ', ' Couple ', ' Deluxe Queen Room with View ', ' Stayed 2 nights ', ' Submitted from a mobile device ']322 day51.5073770.038657Very good and clean. No Negative0.60.90.3leisure1.0couple2.0

Last rows

Hotel_AddressAdditional_Number_of_ScoringReview_DateAverage_ScoreHotel_NameReviewer_NationalityNegative_ReviewReview_Total_Negative_Word_CountsTotal_Number_of_ReviewsPositive_ReviewReview_Total_Positive_Word_CountsTotal_Number_of_Reviews_Reviewer_Has_GivenReviewer_ScoreTagsdays_since_reviewlatlngReviewReview_sentPositive_Review_sentNegative_Review_sentTravel_typeFrom_MobileN_PersonsStayed_Nights
16990Provincialeweg 38 Zuidoost 1108 AB Amsterdam Netherlands2451/28/20177.5Golden Tulip Amsterdam RiversideCzech RepublicNo Negative02362Excellent service and receptive staff6.02.010.0[' Leisure trip ', ' Couple ', ' Standard Twin Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']187 day52.3154534.996300Excellent service and receptive staff. No Negative0.60.90.3leisure1.0couple1.0
16991Tiefer Graben 14 20 01 Innere Stadt 1010 Vienna Austria1532/16/20168.9Hotel Das TigraCzech RepublicNo Negative01817the stuff was nice and helfull7.05.010.0[' Leisure trip ', ' Family with young children ', ' Comfort Double Room ', ' Stayed 1 night ']534 day48.21217716.368077the stuff was nice and helfull. No Negative0.50.80.3leisure0.0family1.0
16992Rec Comtal 16 18 Ciutat Vella 08003 Barcelona Spain2112/24/20178.8Catalonia BornCzech RepublicPillows22094Breakfast Location3.014.09.6[' Business trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ']160 day41.3896922.179866Breakfast Location. Pillows0.10.20.0business0.0couple2.0
16993Via Lentasio 3 Milan City Center 20122 Milan Italy1725/28/20177.7Best Western Hotel AscotCzech RepublicNo Negative01741Very friendly and helpful personal6.018.09.2[' Business trip ', ' Solo traveler ', ' Standard Single Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']67 days45.4580149.191368Very friendly and helpful personal. No Negative0.60.90.3business1.0solo2.0
1699459 Leinster Square Kensington and Chelsea London W2 4PS United Kingdom3376/10/20177.6New Linden HotelCzech RepublicExtremely small room Continental breakfast Uncomfortable bed92820Quiet street in a good lovation7.015.04.6[' Business trip ', ' Solo traveler ', ' Standard Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']54 days51.513383-0.192662Quiet street in a good lovation. Extremely small room Continental breakfast Uncomfortable bed0.10.9-0.6business1.0solo2.0
16995Delflandlaan 15 Slotervaart 1062 EA Amsterdam Netherlands9478/31/20158.7Best Western Premier Hotel CoutureCzech RepublicMinibar not working Parking lot is not free98177Overall cleanness Good location New furnishings Nespresso in the room11.02.09.6[' Leisure trip ', ' Couple ', ' Standard Double or Twin Room ', ' Stayed 4 nights ']703 day52.3511144.841163Overall cleanness Good location New furnishings Nespresso in the room. Minibar not working Parking lot is not free0.00.7-0.7leisure0.0couple4.0
16996Rooseveltplatz 15 09 Alsergrund 1090 Vienna Austria1655/3/20168.4Hotel ReginaCzech Republicsingle room is really small and I do not believe it is 4 limited breakfast161948perfect location3.012.06.7[' Business trip ', ' Solo traveler ', ' Single Room ', ' Stayed 2 nights ']457 day48.21633416.359554perfect location. single room is really small and I do not believe it is 4 limited breakfast0.10.9-0.7business0.0solo2.0
16997152 Cricklewood Broadway Cricklewood London NW2 3ED United Kingdom51211/30/20158.0Clayton Crown Hotel LondonCzech Republicit was little bit cold inside of our room and the only temperature regulation device was from A C which did not realy heat Wifi signal was very weak in our room We had to go to the lobby to be well conneceted from neighboring rooms you here more than you wish Noise isolation is weak582491we were very happy with kind hotel stuff very good connection to public transportation hotel is nice clean and stylish even you need to pay extra for breakfast it is worth money it costs Wide breakfast offer should satisfy everyone even I wrote some negatives in general we can recommend this hotel and we will probably consider this hotel again for our next stay in London68.011.08.3[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 5 nights ']612 day51.556155-0.214182we were very happy with kind hotel stuff very good connection to public transportation hotel is nice clean and stylish even you need to pay extra for breakfast it is worth money it costs Wide breakfast offer should satisfy everyone even I wrote some negatives in general we can recommend this hotel and we will probably consider this hotel again for our next stay in London . it was little bit cold inside of our room and the only temperature regulation device was from A C which did not realy heat Wifi signal was very weak in our room We had to go to the lobby to be well conneceted from neighboring rooms you here more than you wish Noise isolation is weak0.00.9-0.7leisure0.0couple5.0
16998Pelzgasse 1 15 Rudolfsheim F nfhaus 1150 Vienna Austria373/13/20178.3Arthotel ANA WestbahnCzech RepublicNo Negative0450i love everything I have done a lot of traveling through Europe and this is the best hotel I have found so far25.01.010.0[' Leisure trip ', ' Solo traveler ', ' Single Room ', ' Stayed 1 night ']143 day48.19795116.336318i love everything I have done a lot of traveling through Europe and this is the best hotel I have found so far . No Negative0.60.90.3leisure0.0solo1.0
16999Taborstra e 12 02 Leopoldstadt 1020 Vienna Austria40410/31/20169.1Hotel StefanieCzech RepublicOur room on the street side was bit noisy especially when trams were going Extra charge for parking in the hotel233883Good location within walking distance to the centre including Prater Friendly staff Great restaurant called Mochi next to the hotel22.02.08.3[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ']276 day48.21427716.380178Good location within walking distance to the centre including Prater Friendly staff Great restaurant called Mochi next to the hotel . Our room on the street side was bit noisy especially when trams were going Extra charge for parking in the hotel0.00.6-0.7leisure0.0couple2.0

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Hotel_AddressAdditional_Number_of_ScoringReview_DateAverage_ScoreHotel_NameReviewer_NationalityNegative_ReviewReview_Total_Negative_Word_CountsTotal_Number_of_ReviewsPositive_ReviewReview_Total_Positive_Word_CountsTotal_Number_of_Reviews_Reviewer_Has_GivenReviewer_ScoreTagsdays_since_reviewlatlngReviewReview_sentPositive_Review_sentNegative_Review_sentTravel_typeFrom_MobileN_PersonsStayed_Nightscount
0167 rue de Rome 17th arr 75017 Paris France1110/18/20166.8Villa EugenieIsraelNo Negative0165the room was very french and beautiful it was good location and I enjoyed to stay there18.01.09.2[' Leisure trip ', ' Couple ', ' Twin Room ', ' Stayed 3 nights ']289 day48.8871282.314205the room was very french and beautiful it was good location and I enjoyed to stay there. No Negative0.60.90.3leisure0.0couple3.02
140 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France2282/29/20167.9H tel Concorde MontparnasseLebanonNothing32515Nearly everything was perfect5.016.09.2[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']521 day48.8381082.318669Nearly everything was perfect. Nothing0.20.9-0.4leisure1.0couple3.02
240 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France2289/22/20157.9H tel Concorde MontparnasseLebanonThe wifi is horrible Always something is needed in the room and it takes 24 hours before you can get it Room service is bad I ve asked for a bottle of water it came without glasses tray or ic412515The location is half an hour walking to saint germain and the staff is friendly16.03.07.5[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']681 day48.8381082.318669The location is half an hour walking to saint germain and the staff is friendly. The wifi is horrible Always something is needed in the room and it takes 24 hours before you can get it Room service is bad I ve asked for a bottle of water it came without glasses tray or ic0.00.7-0.7leisure1.0couple1.02